{
 "cells": [
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-21T09:44:46.365775Z",
     "start_time": "2025-02-21T09:44:38.488504Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import silhouette_score"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "source": [
    "np.random.seed(42)  # 为了结果的可重复性设置随机种子\n",
    "X = np.random.rand(50, 1)  # 2维数据\n",
    "X"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-02-21T09:45:42.147861Z",
     "start_time": "2025-02-21T09:45:42.141353Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.37454012],\n",
       "       [0.95071431],\n",
       "       [0.73199394],\n",
       "       [0.59865848],\n",
       "       [0.15601864],\n",
       "       [0.15599452],\n",
       "       [0.05808361],\n",
       "       [0.86617615],\n",
       "       [0.60111501],\n",
       "       [0.70807258],\n",
       "       [0.02058449],\n",
       "       [0.96990985],\n",
       "       [0.83244264],\n",
       "       [0.21233911],\n",
       "       [0.18182497],\n",
       "       [0.18340451],\n",
       "       [0.30424224],\n",
       "       [0.52475643],\n",
       "       [0.43194502],\n",
       "       [0.29122914],\n",
       "       [0.61185289],\n",
       "       [0.13949386],\n",
       "       [0.29214465],\n",
       "       [0.36636184],\n",
       "       [0.45606998],\n",
       "       [0.78517596],\n",
       "       [0.19967378],\n",
       "       [0.51423444],\n",
       "       [0.59241457],\n",
       "       [0.04645041],\n",
       "       [0.60754485],\n",
       "       [0.17052412],\n",
       "       [0.06505159],\n",
       "       [0.94888554],\n",
       "       [0.96563203],\n",
       "       [0.80839735],\n",
       "       [0.30461377],\n",
       "       [0.09767211],\n",
       "       [0.68423303],\n",
       "       [0.44015249],\n",
       "       [0.12203823],\n",
       "       [0.49517691],\n",
       "       [0.03438852],\n",
       "       [0.9093204 ],\n",
       "       [0.25877998],\n",
       "       [0.66252228],\n",
       "       [0.31171108],\n",
       "       [0.52006802],\n",
       "       [0.54671028],\n",
       "       [0.18485446]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-02-21T09:45:45.496582Z",
     "start_time": "2025-02-21T09:45:45.492361Z"
    }
   },
   "cell_type": "code",
   "source": "X.shape",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 1)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 3
  },
  {
   "cell_type": "code",
   "source": [
    "X[0,0]=3\n",
    "X[1,0]=2"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-02-21T09:45:45.965316Z",
     "start_time": "2025-02-21T09:45:45.962538Z"
    }
   },
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 假设df是一个Pandas DataFrame，包含了你想要分析的数据集\n",
    "# 'column_name'是你想要检测异常值的列名\n",
    "\n",
    "#随机一个dataframe\n",
    "df=pd.DataFrame(X,columns=['column_name'])\n",
    "\n",
    "# 设置分位数，例如四分位数\n",
    "Q1 = df['column_name'].quantile(0.25)  # 第一四分位数（25%）\n",
    "Q3 = df['column_name'].quantile(0.75)  # 第三四分位数（75%）\n",
    "\n",
    "# 计算四分位数范围（IQR）\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# 定义下界和上界来识别异常值\n",
    "lower_bound = Q1 - 1.5 * IQR\n",
    "upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "# 标记异常值\n",
    "df['outlier'] = df['column_name'].apply(lambda x: 'Yes' if x < lower_bound or x > upper_bound else 'No')\n",
    "\n",
    "# 打印出被标记为异常值的行\n",
    "df[df['outlier'] == 'Yes']\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-02-21T09:45:46.377229Z",
     "start_time": "2025-02-21T09:45:46.366368Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   column_name outlier\n",
       "0          3.0     Yes\n",
       "1          2.0     Yes"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column_name</th>\n",
       "      <th>outlier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn.datasets import make_blobs\n",
    "n_samples = 1500\n",
    "random_state = 170\n",
    "X, y = make_blobs(n_samples=n_samples, random_state=random_state) \n",
    "print(X.shape)\n",
    "print('-'*50)\n",
    "rng = np.random.RandomState(74)\n",
    "X = np.concatenate([X, rng.uniform(low=-10, high=10, size=(100, 2))])\n",
    "print(X.shape) \n",
    "plt.scatter(X[:, 0], X[:, 1], s=5)\n",
    "plt.title(\"Generated Data with Noise\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-02-21T09:46:25.658513Z",
     "start_time": "2025-02-21T09:46:25.334985Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1500, 2)\n",
      "--------------------------------------------------\n",
      "(1600, 2)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "source": [
    "X[0:5]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-02-21T10:36:18.510856Z",
     "start_time": "2025-02-21T10:36:18.506288Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -5.19811282,   0.64186932],\n",
       "       [ -5.75229538,   0.41862711],\n",
       "       [-10.84489837,  -7.55352273],\n",
       "       [ -4.57098483,  -0.80101741],\n",
       "       [ -3.51916215,   0.0393449 ]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "source": [
    "# 使用DBSCAN算法\n",
    "db = DBSCAN(eps=0.5, min_samples=5).fit(X) \n",
    "labels = db.labels_ \n",
    "print(np.unique(labels))\n",
    "noise_mask = (labels == -1)\n",
    "print(noise_mask[0:5])\n",
    "print(np.sum(noise_mask)) \n",
    "print(np.unique(labels[~noise_mask])) \n",
    "plt.scatter(X[~noise_mask, 0], X[~noise_mask, 1], c=labels[~noise_mask], s=5, cmap='viridis') \n",
    "plt.scatter(X[noise_mask, 0], X[noise_mask, 1], c='red', s=5, label='Noise') \n",
    "plt.title(\"DBSCAN Clustering\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-02-21T10:36:25.878529Z",
     "start_time": "2025-02-21T10:36:25.756330Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1  0  1  2]\n",
      "[False False  True False False]\n",
      "131\n",
      "[0 1 2]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from sklearn.ensemble import IsolationForest\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "np.random.seed(42)  \n",
    "X = np.random.rand(50, 2)  \n",
    "\n",
    "iso_forest = IsolationForest(random_state=42, contamination=0.1)\n",
    "iso_forest.fit(X)\n",
    "scores = iso_forest.decision_function(X)\n",
    "print(scores)\n",
    "labels = iso_forest.predict(X)\n",
    "print(np.unique(labels))\n",
    "plt.scatter(X[:, 0], X[:, 1], c=labels, cmap=plt.cm.bwr, edgecolor='k', s=50)\n",
    "plt.title('Isolation Forest Anomaly Detection')\n",
    "plt.xlabel('Feature 1')\n",
    "plt.ylabel('Feature 2')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-02-21T11:18:21.635185Z",
     "start_time": "2025-02-21T11:18:21.265329Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00105308  0.10055646  0.05361504  0.05024246  0.07184847  0.00404901\n",
      "  0.01997561  0.0627385   0.10884232  0.0783621   0.0611147   0.08402063\n",
      "  0.04548452  0.07878843  0.00254178  0.08008609  0.0508405  -0.01082588\n",
      "  0.06452119  0.0800867   0.04634851  0.04934762  0.06599273  0.10829868\n",
      "  0.07446152 -0.01120788 -0.05423651  0.00130106  0.02542394  0.00763327\n",
      "  0.07608262  0.04074583  0.09872945  0.05188861 -0.00947773  0.0588507\n",
      "  0.01964547  0.0595183   0.00608521  0.06445908  0.03844004  0.03177732\n",
      "  0.09384891  0.09728622  0.01367941  0.04172716  0.09544099  0.09193243\n",
      "  0.06956676 -0.02362362]\n",
      "[-1  1]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "source": [
    "df=pd.DataFrame(np.c_[X,scores,labels],columns=['Feature 1','Feature 2','scores','labels'])\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-02-21T13:26:23.819402Z",
     "start_time": "2025-02-21T13:26:23.808297Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "    Feature 1  Feature 2    scores  labels\n",
       "0    0.374540   0.950714  0.001053     1.0\n",
       "1    0.731994   0.598658  0.100556     1.0\n",
       "2    0.156019   0.155995  0.053615     1.0\n",
       "3    0.058084   0.866176  0.050242     1.0\n",
       "4    0.601115   0.708073  0.071848     1.0\n",
       "5    0.020584   0.969910  0.004049     1.0\n",
       "6    0.832443   0.212339  0.019976     1.0\n",
       "7    0.181825   0.183405  0.062738     1.0\n",
       "8    0.304242   0.524756  0.108842     1.0\n",
       "9    0.431945   0.291229  0.078362     1.0\n",
       "10   0.611853   0.139494  0.061115     1.0\n",
       "11   0.292145   0.366362  0.084021     1.0\n",
       "12   0.456070   0.785176  0.045485     1.0\n",
       "13   0.199674   0.514234  0.078788     1.0\n",
       "14   0.592415   0.046450  0.002542     1.0\n",
       "15   0.607545   0.170524  0.080086     1.0\n",
       "16   0.065052   0.948886  0.050840     1.0\n",
       "17   0.965632   0.808397 -0.010826    -1.0\n",
       "18   0.304614   0.097672  0.064521     1.0\n",
       "19   0.684233   0.440152  0.080087     1.0\n",
       "20   0.122038   0.495177  0.046349     1.0\n",
       "21   0.034389   0.909320  0.049348     1.0\n",
       "22   0.258780   0.662522  0.065993     1.0\n",
       "23   0.311711   0.520068  0.108299     1.0\n",
       "24   0.546710   0.184854  0.074462     1.0\n",
       "25   0.969585   0.775133 -0.011208    -1.0\n",
       "26   0.939499   0.894827 -0.054237    -1.0\n",
       "27   0.597900   0.921874  0.001301     1.0\n",
       "28   0.088493   0.195983  0.025424     1.0\n",
       "29   0.045227   0.325330  0.007633     1.0\n",
       "30   0.388677   0.271349  0.076083     1.0\n",
       "31   0.828738   0.356753  0.040746     1.0\n",
       "32   0.280935   0.542696  0.098729     1.0\n",
       "33   0.140924   0.802197  0.051889     1.0\n",
       "34   0.074551   0.986887 -0.009478    -1.0\n",
       "35   0.772245   0.198716  0.058851     1.0\n",
       "36   0.005522   0.815461  0.019645     1.0\n",
       "37   0.706857   0.729007  0.059518     1.0\n",
       "38   0.771270   0.074045  0.006085     1.0\n",
       "39   0.358466   0.115869  0.064459     1.0\n",
       "40   0.863103   0.623298  0.038440     1.0\n",
       "41   0.330898   0.063558  0.031777     1.0\n",
       "42   0.310982   0.325183  0.093849     1.0\n",
       "43   0.729606   0.637557  0.097286     1.0\n",
       "44   0.887213   0.472215  0.013679     1.0\n",
       "45   0.119594   0.713245  0.041727     1.0\n",
       "46   0.760785   0.561277  0.095441     1.0\n",
       "47   0.770967   0.493796  0.091932     1.0\n",
       "48   0.522733   0.427541  0.069567     1.0\n",
       "49   0.025419   0.107891 -0.023624    -1.0"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature 1</th>\n",
       "      <th>Feature 2</th>\n",
       "      <th>scores</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.374540</td>\n",
       "      <td>0.950714</td>\n",
       "      <td>0.001053</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.731994</td>\n",
       "      <td>0.598658</td>\n",
       "      <td>0.100556</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.156019</td>\n",
       "      <td>0.155995</td>\n",
       "      <td>0.053615</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.058084</td>\n",
       "      <td>0.866176</td>\n",
       "      <td>0.050242</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.601115</td>\n",
       "      <td>0.708073</td>\n",
       "      <td>0.071848</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.020584</td>\n",
       "      <td>0.969910</td>\n",
       "      <td>0.004049</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.832443</td>\n",
       "      <td>0.212339</td>\n",
       "      <td>0.019976</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.181825</td>\n",
       "      <td>0.183405</td>\n",
       "      <td>0.062738</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.304242</td>\n",
       "      <td>0.524756</td>\n",
       "      <td>0.108842</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.431945</td>\n",
       "      <td>0.291229</td>\n",
       "      <td>0.078362</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.611853</td>\n",
       "      <td>0.139494</td>\n",
       "      <td>0.061115</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.292145</td>\n",
       "      <td>0.366362</td>\n",
       "      <td>0.084021</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.456070</td>\n",
       "      <td>0.785176</td>\n",
       "      <td>0.045485</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.199674</td>\n",
       "      <td>0.514234</td>\n",
       "      <td>0.078788</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.592415</td>\n",
       "      <td>0.046450</td>\n",
       "      <td>0.002542</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.607545</td>\n",
       "      <td>0.170524</td>\n",
       "      <td>0.080086</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.065052</td>\n",
       "      <td>0.948886</td>\n",
       "      <td>0.050840</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.965632</td>\n",
       "      <td>0.808397</td>\n",
       "      <td>-0.010826</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.304614</td>\n",
       "      <td>0.097672</td>\n",
       "      <td>0.064521</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.684233</td>\n",
       "      <td>0.440152</td>\n",
       "      <td>0.080087</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.122038</td>\n",
       "      <td>0.495177</td>\n",
       "      <td>0.046349</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.034389</td>\n",
       "      <td>0.909320</td>\n",
       "      <td>0.049348</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.258780</td>\n",
       "      <td>0.662522</td>\n",
       "      <td>0.065993</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.311711</td>\n",
       "      <td>0.520068</td>\n",
       "      <td>0.108299</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.546710</td>\n",
       "      <td>0.184854</td>\n",
       "      <td>0.074462</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.969585</td>\n",
       "      <td>0.775133</td>\n",
       "      <td>-0.011208</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.939499</td>\n",
       "      <td>0.894827</td>\n",
       "      <td>-0.054237</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.597900</td>\n",
       "      <td>0.921874</td>\n",
       "      <td>0.001301</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.088493</td>\n",
       "      <td>0.195983</td>\n",
       "      <td>0.025424</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.045227</td>\n",
       "      <td>0.325330</td>\n",
       "      <td>0.007633</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.388677</td>\n",
       "      <td>0.271349</td>\n",
       "      <td>0.076083</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.828738</td>\n",
       "      <td>0.356753</td>\n",
       "      <td>0.040746</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.280935</td>\n",
       "      <td>0.542696</td>\n",
       "      <td>0.098729</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.140924</td>\n",
       "      <td>0.802197</td>\n",
       "      <td>0.051889</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.074551</td>\n",
       "      <td>0.986887</td>\n",
       "      <td>-0.009478</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.772245</td>\n",
       "      <td>0.198716</td>\n",
       "      <td>0.058851</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.005522</td>\n",
       "      <td>0.815461</td>\n",
       "      <td>0.019645</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.706857</td>\n",
       "      <td>0.729007</td>\n",
       "      <td>0.059518</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.771270</td>\n",
       "      <td>0.074045</td>\n",
       "      <td>0.006085</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.358466</td>\n",
       "      <td>0.115869</td>\n",
       "      <td>0.064459</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.863103</td>\n",
       "      <td>0.623298</td>\n",
       "      <td>0.038440</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.330898</td>\n",
       "      <td>0.063558</td>\n",
       "      <td>0.031777</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.310982</td>\n",
       "      <td>0.325183</td>\n",
       "      <td>0.093849</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.729606</td>\n",
       "      <td>0.637557</td>\n",
       "      <td>0.097286</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.887213</td>\n",
       "      <td>0.472215</td>\n",
       "      <td>0.013679</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.119594</td>\n",
       "      <td>0.713245</td>\n",
       "      <td>0.041727</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.760785</td>\n",
       "      <td>0.561277</td>\n",
       "      <td>0.095441</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.770967</td>\n",
       "      <td>0.493796</td>\n",
       "      <td>0.091932</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.522733</td>\n",
       "      <td>0.427541</td>\n",
       "      <td>0.069567</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.025419</td>\n",
       "      <td>0.107891</td>\n",
       "      <td>-0.023624</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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